Sleepiness and the risk of road accidents for professional drivers: A systematic review and meta-analysis of retrospective studies

Safety Science 70 (2014) 180–188

Contents lists available at ScienceDirect

Safety Science journal homepage: www.elsevier.com/locate/ssci

Review

Sleepiness and the risk of road accidents for professional drivers: A systematic review and meta-analysis of retrospective studies Tingru Zhang ⇑, Alan H.S. Chan Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong

a r t i c l e

i n f o

Article history: Received 23 January 2014 Received in revised form 28 April 2014 Accepted 30 May 2014

Keywords: Sleepiness Road crash risk Professional driver Effect size Meta-analysis

a b s t r a c t This paper reports the results of a search and review of available evidence on the risk of road crashes associated with sleepiness for professional drivers. Summary of the effects were grouped according to different sleepiness-inducing factors. Meta-analysis suggested that modestly increased accident risks were associated with excessive daytime sleepiness (EDS), sleep apnea and acute sleepiness, but not significantly associated with insomnia. Compared with non-professional drivers, sleep apnea had a smaller effect size and excessive daytime sleepiness had a similar effect size on professional drivers. Effects of other sleepiness inducing factors such as sleep debt, sleep quality and snoring were also presented without meta-analysis. Conducting this review revealed that the main problems in this research field were the limited number of studies available, poor control for confounding factors, failure to take severity levels into account and incomplete reporting. Ó 2014 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Comprehensive literature search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Coding of studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Study characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Sleep problem information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3. Accident information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4. Estimates of effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1. Effect size unification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2. Summary effect estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Main characteristics of included studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Study quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1. Description of sleep problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2. Accident data collection methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3. Control for confounding factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Sleepiness-related factors and crash risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1. Excessive daytime sleepiness (EDS), sleep apnea, acute sleepiness, and insomnia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2. Other sleepiness-related risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3. Potential confounding factor impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Sleep apnea (SA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Excessive daytime sleepiness (EDS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

⇑ Corresponding author. Tel.: +852 5983 2370. E-mail address: [email protected] (T. Zhang). http://dx.doi.org/10.1016/j.ssci.2014.05.022 0925-7535/Ó 2014 Elsevier Ltd. All rights reserved.

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5.

4.3. Acute sleepiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Limitations and future research direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction The have been many studies on the role of sleepiness in road traffic accidents (RTAs) and the estimated proportion of car crashes attributable to sleepiness ranges from 2% in Norway (Phillips and Sagberg, 2013) to 25% in Australia (Naughton and Pierce, 1991). More serious are the findings that sleepiness accounts for an even greater proportion of accidents involving deaths or serious injuries. It was found that about 20% of fatal accidents were related to sleepiness (Road and Traffic Authority, 2001), and before that another study indicated that between 29% and 50% of road deaths and serious injuries were caused by sleep-related RTAs (Dawson and Reid, 1997). Professional drivers are believed to be more likely to be involved in crashes caused by sleepiness compared to nonprofessional drivers (Bunn et al., 2005). The cost of drowsy driving has been estimated as $12.4 billion per year by the United States National Highway Traffic Safety Administration (Wang et al., 1996). However, as the real extent of the sleep problem is generally agreed to be underestimated (Dinges, 1995), sleepiness actually causes greater loss than generally estimated. Sleepiness can be triggered by a variety of factors such as acute or chronic sleep deprivation, shift work or other circadian rhythm disturbances, sleep disorders as well as physical or psychological diseases (e.g. diabetes, heart disease, and depression). Road crash risks associated with the factors that may induce sleepiness have been explored (Chipman and Jin, 2009) and summarized in several review papers (Connor et al., 2001; Ellen et al., 2006; Tregear et al., 2009; Vaa, 2003). According to the available reviews, while there has been clear evidence that drivers with sleep apnea had a high risk of having accidents, the effects of other sleepiness-related factors were unclear due to the inconsistent conclusions from different studies. Two of the reviews (Connor et al., 2001; Vaa, 2003) used non-professional drivers as the target group; the other two (Ellen et al., 2006; Tregear et al., 2009) tried to separate out the effects of sleepiness on professional drivers, however, they did not arrive at quantitative conclusions for professional drivers. Compared with non-professional drivers and other occupations, the work of professional drivers is characterized by sedentary restricted postures, monotonous long-hours of driving, irregular work shifts and a unique working environment (Bunn et al., 2005; Öz et al., 2010). The nature of the work of professional drivers makes it reasonable to hypothesize that sleepiness problems may have different frequency, pattern, severity and thus different effects on professional drivers when compared to other occupations. Sleep apnea, insomnia, and excessive daytime sleepiness (EDS) have been shown to be more common among professional drivers than other occupations. Taking sleep apnea as an example, a conservative estimation of its prevalence was found to be approximately 3–7% in the general population (Punjabi, 2008). However, a study done by Pack et al. (2006) identified that about 28.2% of professional drivers suffered mild and another 4.7% suffered severe sleep apnea. Another two independent studies both concluded that 10% of bus drivers from the UK (Vennelle et al., 2010) and Hong Kong (Hui et al., 2006) may suffer from sleep apnea. There have been an increasing number of sleepiness accident studies focusing specifically on professional drivers in recent years,

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187 187 187 188 188

which makes it possible to conduct a systematic review on the association between professional driver sleepiness and accidents. Moreover, increasing attention on the safety and health of professional drivers makes such a review timely and meaningful. Hence, the aim of this paper is to summarize current knowledge of the association between sleepiness and road accident risk for professional drivers and to compare this association to what has been found for non-professional drivers in order to determine whether professional drivers are at higher risk of accidents due to sleepiness. Throughout this paper, the term ‘professional drivers’ will be used to denote ‘occupational drivers’ as well as ‘commercial drivers’ to describe drivers who have commercial driver licenses and are required to perform safety-sensitive driving functions as their primary work assignment. Summaries were made for each type of sleepiness inducement factor and the factors were not merged together because of the possible large differences between the effects of the different sleepiness inducing factors. For sleepiness factors with enough available data, meta-analysis was applied to generate summaries of the size of the effects; and for those factors with more limited material available, the effects from different studies were reported but without statistical combination. 2. Methods 2.1. Comprehensive literature search In order to get a complete and unbiased understanding of the impact of sleepiness on crash risk, reports from journals, books, conferences as well as theses and doctoral dissertations were candidates for review. Electronic databases including MEDLINE, PubMed, ScienceDirect and Google Scholar were searched using keywords: sleepiness AND (traffic accident OR crash) AND (professional drivers OR occupational drivers OR truck OR lorry OR commercial OR taxi OR bus). These keywords were determined by referring to previous work on reviews and by comparing the effectiveness of the candidate words. To determine whether or not a study was included in the review the criteria listed in Table 1 were used. A total of 239 articles were collected from the initial search and after reading the abstracts, 163 articles were excluded since they were not sleepiness-accident related studies. Another 61 articles were excluded due to not satisfying criteria 1–4 (Table 1). The result was that 15 papers were retained for review. The database searches and the selection work were performed by the first author. 2.2. Coding of studies For each study included, information for the following characteristics was extracted. 2.2.1. Study characteristics Three variables, study ID, country where the study was conducted and the sample size, were included in this category. Study ID was recorded as the name of the authors and publication year; sample size was the number of professional drivers studied.

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Table 1 Inclusion criteria. Criteria for inclusion in review 1. Article should be published in English language 2. Impact of sleepiness on crash risk for professional drivers should be an output of the article. For studies using both non-professional and professional drivers as research groups, only those studies where the crash association for professional drivers was available separately were included 3. Sleepiness is represented and measured by one or combinations of the following factors: sleep disorders, excessive daytime sleepiness, insomnia, sleepiness at wheel, sleep quality and quantity, work factors and snoring. These factors are common sleepiness inducements and have received a lot of attention. Other sleepiness related factors were not reviewed 4. Studies must contain enough information for extraction or estimation of the size of the effect and its 95% confidence interval (CI). The effect size should be in the form of correlation, Cohen’s d or odds ratio. For studies that claim ‘‘no effect’’ or ‘‘no significant effect’’ without offering specific values, they are still included because if not included, the estimated effect size would be larger than it really is. Methods to deal with such data are introduced as part of the meta-analysis

2.2.2. Sleep problem information The names of sleepiness inducements and diagnostic criteria were extracted. Information of severity of sleep problem was not extracted as an independent variable but was reflected in the name of sleep problem. For example, one paper distinguished severe and moderate excessive daytime sleepiness and the two situations were recorded as severe EDS and moderate EDS; papers without such discrimination were uniformly recorded as EDS. 2.2.3. Accident information This category included the accident period studied, specific accident type, and methods used to collect accident data. Studies generally investigated accident experience within a certain time period (e.g. previous year, last 5 years or driving history) and they may only focus on certain types of accidents (e.g. sleepy accident only or multiple-vehicle accident only). Accident data could be gathered through drivers’ self-reports, from company records or from police databases.

transformations necessary and to allow comparisons with the findings from other studies, most of which have reported odds ratios. The transformation methods described in Borenstein et al. (2009) were applied. The correlation (r) was first converted to Cohen’s d based on Eqs. (1) and (2) and the Cohen’s d was further converted into odds ratio statistic (logarithmic form of odds ratio was used during transformation and then was converted back to odds ratio) through Eqs. (3) and (4).

2r d ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffi 1  r2 Vd ¼

4V r ð1  r 2 Þ

2.3. Meta-analysis 2.3.1. Effect size unification Different types of effect sizes were reported across all the studies included in this review. Eleven studies reported odds ratios directly or contained crosstabs from which ORs could be calculated; two studies (Souza et al., 2005; Stoohs et al., 1994) reported means and standard deviations (SDs), from which the Cohen’s d statistic could be inferred; the remaining studies (Akkoyunlu et al., 2013; Razmpa et al., 2011) used correlation to represent the effect size. To make comparisons between studies and generate summarized effects, other forms of effect sizes were first translated into odds ratios. Odds ratio, considered as the best choice for a meta-analysis (Borenstein et al., 2009), was chosen as a standardized measure in this research to reduce the number of

3

p

Log OR ¼ d pffiffiffi 3 V LogOR ¼ V d

2.2.4. Estimates of effects For each study, the type of effect size used (correlation, Cohen’s d or odds ratio), its value, and the confounders controlled (e.g. age) were extracted. The details of coding information are listed in Table 2.

ð1Þ

p2 3

ð2Þ

ð3Þ

ð4Þ

where d represents Cohen’s d; r represents the correlation; Log OR represents the log odds ratio; V r , V d , and V LogOR represents the variance of the r, Cohen’s d, and LogOR, respectively. After performing the above steps, uniform effect sizes as well as the confidence intervals for each study could be generated. One point to take note of was the processing of the unreported nonsignificant effect size. It was quite common for authors to indicate a non-significant association but to not offer any statistical details (e.g., OR, p-value or t-value). Some previous meta-analysis reviews have not included such studies because of the omission of key effect size statistics, with the result that there was a tendency to generate larger average effects than would otherwise be the case. A way to eliminate the upward bias is to assign a zero effect size to the insignificant impact (Caird et al., 2008) and this was the method used in this review to control the potential upward bias. 2.3.2. Summary effect estimation The sample odds ratio has a skewed distribution, making it difficult to estimate confidence intervals (Bland and Altman, 2000). One common method to deal with this problem is to carry out

Table 2 Coding information. Variable coded

Codes meaning

Sleep problems Accident type

AS = acute sleepiness; EDS = excessive daytime sleepiness; Insomnia; PSQ = poor sleep quality; SA = sleep apnea; SD = sleep debt; Snoring 1 = any types of accidents; 2 = sleepy accident; 3 = sleepy near miss; 4 = at work accident; 5 = single accident; 6 = multiple accident; 7 = casualty accident; 8 = fatal accident; 9 = non-fatal accident OR = odds ratio; r = correlation; d = Cohen’s d C = company record; P = police record; S = self-report a = age; b = mileage; c = driving experience; d = BMI (Body mass index); e = alcohol; f = drug use; g = gender; h = others

Risk estimator Data collection Confounders controlled Detect method

AHI = apnea-hypopnea index; BMI = body mass index; ESS = Epworth Sleepiness Scale; MAP = multivariable apnea prediction; ODI = oxygen desaturation index; PSQI = Pittsburgh Sleep Quality Index

T. Zhang, A.H.S. Chan / Safety Science 70 (2014) 180–188

all calculation on a log scale (Log OR) since the logarithmic format of odds ratio is normally distributed. The log summary effect (Log SE) is calculated as the weighted mean of all effects (Eq. (5)). Final log results are then converted back into the original metric at the end of the analysis.

Pk Log SE ¼

 i¼1 W i Log OR i Pk i¼1 W i

Wi ¼

1 V LogORi 1 V LogORi þ T 2

assessed by Pittsburgh Sleep Quality Index (PSQI); for sleep apnea, Berlin questionnaire and Multivariable Apnea Prediction Score were used. Only three studies (Akkoyunlu et al., 2013; Karimi et al., 2013; Stoohs et al., 1994) used objective polysomnography to detect sleep apnea and it is to be expected that their diagnosis should be of higher accuracy than results from self-reported data.

ð5Þ

where W i is the statistic weight assigned to each study. In fixed effect model, its value equals the inverse of variance of logarithm of odds ratios (Eq. (6)); in random effect model, its value is the inverse of sum of variance of log odds ratio and between studies variance (T2) (Eq. (7)).

Wi ¼

183

ð6Þ

ð7Þ

3. Results 3.1. Main characteristics of included studies Data were extracted from the 15 included studies independently by two analysts based on the coding system introduced in Section 2.2. The two analysts disagreed on whether information for obesity should be extracted. It was then decided by the third reviewer (the first author) to extract the effect sizes of obesity given its close association with sleepiness-related problems. The summary of the extracted data is listed in Table 3. Fourteen out of the 15 papers were published in past ten years, and all of them were published in refereed journals (e.g. ‘Accident Analysis and Prevention’, ‘Injury Prevention’ or ‘Sleep and Breathing’). Excessive daytime sleepiness was the most widely studied sleepinessinducement factor with eleven studies investigating its association with road crash risk. Sleep apnea, one common form of sleep disorder, had also been studied by a relatively large number of researchers. Other sources of sleepiness such as insomnia, sleep deprivation, sleep quality, snoring symptoms and acute sleepiness at the wheel were also investigated. To quantify accident risk associated with these sleepiness factors, different analysis methods were applied based on the forms of the dependent variable (i.e. measurement of accident involvement) and these generated different forms of effect sizes (correlation, Cohen’s d or odds ratio). The details for each study are listed in Table 3.

3.2.2. Accident data collection methods Accident type, period studied and accident data collection method can influence the results of meta-analysis. It has been recommended that a clear distinction should be made between fatal, injury-involved, property-damage-only crashes and near-misses, because there has been evidence that risks associated with sleepiness vary according to the severity of the crash (Connor et al., 2001). Only three of the reviewed studies considered the influence of crash severity and actually limited their studies to a particular crash severity (Bunn et al., 2005; Sabbagh-Ehrlich et al., 2005; Stuckey et al., 2010). Those that did not distinguish severity either treated all types of accidents as one integrated group or used other classification criteria. For example, Carter et al. (2003) separated effects for work-related driving accidents and leisure traffic accidents and found that sleepiness might have different impacts on these two kinds of accidents. Howard et al. (2004) classified accidents based on number of vehicles involved (single accident or multiple accident) and concluded that sleep problems had varied influences. For data collection method, most studies used selfreported data. The only three exceptions used police records or company records (Bunn et al., 2005; Stoohs et al., 1994; Stuckey et al., 2010). When used for accident data collection, the self-report method has received a lot of criticism including recall bias, deliberate fake answers and inconsistent criteria. However, the self-report method is still widely applied because it is easy to implement. Records from police, driver company databases or other official channel are considered to be more objective and accurate. 3.2.3. Control for confounding factors Poor control for potentially confounding factors is likely to exaggerate the risk associated with the factors studied (Elvik, 2011). Approximately half of the studies considered and controlled for potential confounders. Age, driving experience, BMI have been proved to be associated with road risk by research on nonprofessional drivers and thus are assumed to be also associated with crash risk for professional drivers. They were the three most likely factors to be controlled. Some studies also considered and controlled potential impacts from factors such as gender, alcohol consumption or drug use. Table 3 shows a summary for studies included in this review.

3.2. Study quality

3.3. Sleepiness-related factors and crash risk

A framework for assessing the quality of epidemiological studies focusing on association between drivers’ health and road accidents has been proposed by Elvik (2011). Based on his theory, the quality of the reviewed studies was assessed with regard to the following three aspects: description of sleep problems; accident data collection methods and control for confounding factors.

Initially, it was planned to generate one average effect size for sleepiness on crash risk for professional drivers. However, review of past studies indicated that sleepiness could be triggered for different reasons and the effect sizes for different types of sleep problems are significantly dissimilar (Philip et al., 2010).Thus, here, instead of combining all results, the combined effect for each type of sleepiness inducement was obtained and will be discussed in the following sections.

3.2.1. Description of sleep problems Studies with clear and complete definitions, and diagnosis criteria for the sleep problems were of higher quality compared to studies without. In this paper, all of the studies reviewed clearly stated the sleep problems they investigated and explained the detection methods in detail. Most of the sleep-related problems were diagnosed using widely-accepted questionnaires. For instance, assessment of excessive daytime sleepiness (EDS) was based on Epworth Sleepiness Scale (ESS), sleep quality was

3.3.1. Excessive daytime sleepiness (EDS), sleep apnea, acute sleepiness, and insomnia Risks associated with these four sleepiness factors were separately summarized by meta-analysis since there were enough data from previous studies. The following statistical analysis was performed using the Comprehensive Meta-analysis Software (Borenstein et al., 2005).

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Table 3 Summary for studies included in review. ID

Sample size

Accident period

Effect size type

Risk factor

Detection method

Effect size

Accident type

Confounding Data collection method

Stoohs et al. (1994) USA

90

5 years

Cohen’s d

EDS SA Obesity

Not mentioned ODI> = 10 BMI> = 30

d = 0.81 d = 0.31 d = 0.47

1

NO

S&C

Carter et al. (2003)

1389

Unknown

OR

EDS & snoring EDS & SD EDS & SD

ESS > 10

NS*

1

a,b,e

S

ESS > 10 ESS > 10

OR = 2.4 OR = 2.1

4 3

Severe EDS Severe EDS SA

ESS > 18

OR = 1.91

1

a,e,f,g,h

S

ESS > 18

OR = 2.67

6

MAP>=0.5 & ESS > 10 MAP>=0.5 & ESS > 10 Sleep hours <=5 Sleep hours (5.1–6)

OR = 1.3

1

OR = 1.63

5

OR = 1.05 OR = 1.14

1

a,c,e

Howard et al. (2004)

Country

Sweden

Australia 2342

3 years

OR

SA Severe SD Moderate SD Sabbagh-Ehrlich et al. (2005)

Israel

160

Driving history OR

AS PSQ

Self-report PSQI > 5

OR = 0.6 OR = 2.9

7

c,d,h

S

Perez-Chada et al. (2005)

Spain

738

Driving history OR

EDS AS Snoring

ESS > 10 Self-report Snore more than 3 nights per week Insomnia Index

OR = 2.53 OR = 1.46 OR = 1.73

2&3

a,c,h

S

Bunn et al. (2005)

USA

339

1998–2002

OR

AS

Investigating officer’s conclusion

OR = 21.03 8 VS 9

a,h

P

Souza et al. (2005)

Brazil

260

5 years

Cohen’s d

EDS PSQ Obesity

ESS PSQI > 5 BMI

d = 0.46 d = NS d = NS

1

NO

S

de Pinho (2006)

Brazil

300

Unknown

OR

EDS

ESS > 10

OR = 2.24

1

NO

S

Leechawengwongs et al. (2006)

Thailand

4331

6 months

OR

EDS

ESS > 10

OR = 1.68

1

NO

S

Stuckey et al. (2010)

Australia 13491 accidents data

year 2004

OR

AS

Investigating officer’s conclusion

OR = 2.2

1

a,g

P

Philip et al. (2010)

USA

Previous year

OR

SA Insomnia EDS EDS SA Insomnia

Ever been diagnosed OR = NS OR = NS ESS (11–15) OR = 2.22 ESS > 15 OR = 5.00 ever been diagnosed OR = 2.09 OR = 1.78

1&3

a,b,g,h

S

1

NO

S

Insomnia

*

35004

OR = 1.27

Razmpa et al. (2011)

Iran

175

5 years

Pearson correlation test

EDS SA Insomnia

EES > 10 Apnea Index Insomnia Index

Amra et al. (2012)

Iran

931

driving history

OR

EDS Snoring SA

EES > 10 OR = 0.47 Self-report OR = 1.50 Berlin Questionnaire OR = 0.25

1

1,5,7

S

Karimi et al. (2013) Sweden

101

Past year and past 5 years

OR

SA EDS

AHI > 5 ESS > 10

OR = 2.89 OR = 5.78

1

NO

S

Akkoyunlu et al. (2013)

241

Driving history Pearson correlation

SA

AHI > 5

r = 0.57

1

NO

S

Turkey

NS r = 0.25 NS

2&3

’NS’ represents ‘not significant’.

3.3.1.1. Summary effect using fixed model. The fixed effect model was first applied since it was reasonable to assume that the true effect size was similar for all studies of the same sleep-related problem. For EDS, effect sizes were extracted from 11 studies. Diagnosis criteria for EDS were consistent: drivers with a total score larger than 10 in ESS test were believed to have excessive daytime sleepiness trouble. Two studies further classified EDS into severe or moderate and calculated the effect for each severity (Howard et al., 2004; Philip et al., 2010). In studies where multiple effect sizes for one sleepiness factor were reported, the average

effect size was used in meta-analysis. Such a method is typical and has been used in meta-analysis for many different research areas (Caird et al., 2008; Hunter and Schmidt, 2004). For acute sleepiness, one study (Bunn et al., 2005) was excluded due to the abnormal large variance. For sleep apnea, acute sleepiness and insomnia, the numbers of effect sizes extracted were seven, four and three, respectively. From the forest plot (Fig. 1) of EDS, it was clear that most studies concluded that there was a significantly higher crash risk for professional drivers with EDS. The only exception was noted in

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Fig. 1. Forest plot for EDS. Note: In this plot, the mid-point of a square represents the odds ratio estimated for the study, the area of the square is proportional to weights given to the study in the meta-analysis, and the width of the horizontal line represents the confidence interval. The center of the diamond is the overall odds ratio and the width of the diamond is the confidence interval.

one piece of research exploring accident risk factors in Persian professional vehicle drivers (Amra et al., 2012), which found an OR of 0.47 (95% CI: 0.15–1.4). Summary odds ratio of EDS was 1.89 (95% CI: 1.49–1.92), indicating existence of EDS was associated with increased crash risk. Based on the fixed models, sleep apnea and acute sleepiness had similar moderate impacts on crash risk with OR = 1.75 (95% CI: 1.50–2.05) and OR = 1.85 (95% CI: 1.52–2.24) respectively. The findings from this meta-analysis supported the contention that drivers that suffered from sleep apnea or frequently experienced sleepiness at the wheel were more likely to be involved in traffic accidents compared to drivers without the two problems. However, it should be pointed out that one study generated a contrary result for sleep apnea (OR: 0.25; 95% CI: 0.07–0.84) and this was the same study that was the exception in the EDS review (Amra et al., 2012). For insomnia, meta-analysis did not generate a significant effect size (OR: 1.17; 95% CI: 0.92–1.49). 3.3.1.2. Test for heterogeneity based on Cochran’s Q statistic. The studies reviewed varied in their focus groups (bus driver, taxi driver, other type of professional driver or combination of several types of drivers), the definition of accidents (all type of collision, crashes due to sleepiness, at work and off work crashes), and sleep problem detection methods (clinical diagnosis or self-report). The existence of these differences may cause the true effect size to vary among studies and therefore, heterogeneity was tested based on formulae and theories provided by Borenstein et al. (2011) and Higgins and Thompson (2002). The heterogeneity test results are summarized in Table 4. Although both the Q statistic and I-squared statistic are heterogeneity indicators, the former was sensitive to the number of studies while the latter was independent of both study number and effect size scale. Higgins and Thompson (2002) proposed that an I-squared statistic higher than 75% indicated the existence of heterogeneity. For sleep apnea, both Q statistic and I-squared statistic indicated the existence of heterogeneity and for EDS, the Q statistic showed significance and the I-squared indicated a near significant heterogeneity. For insomnia and sleepiness at the wheel, with only three studies available for each, the I-squared statistic was examined and no heterogeneity was found. Based on the above analysis, odds ratios for both EDS and SA were recalculated using random models. Table 5 shows a summary of the final odds ratios and the models used for each sleep problem group.

3.3.1.3. Test and adjust for publication bias. Publication bias refers to the phenomenon that findings are less likely to be published when they are not statistically significant, against the previously published materials, or are hard to explain. Trim-and-fill analysis can be used to test and adjust for the possible presence of publication bias (Duval and Tweedie, 2000) when at least five estimates of risk are available. This technique detects the possible publication bias by testing for the asymmetry in the funnel plot. A funnel plot is a scatterplot of effect size against a measure of study size (e.g. standard error). When there is no publication bias, the data points in a funnel plot should be symmetrically distributed around the summary estimate (Elvik, 2011). In case of the existence of publication bias, the ‘trim’ uses iterative procedure to remove the most extreme small studies from the positive side of the funnel plot, re-computing the effect size at each iteration until the funnel plot is symmetric about the new effect size (Borenstein et al., 2011). The ‘fill’ process is then applied by adding the removed studies back into the analysis and imputing a mirror image (i.e. the imputed study) for each (Borenstein et al., 2011). For a more detailed technical description of how to perform a trim-and-fill analysis, interested readers can refer to Høye and Elvik (2010). In this review, only estimates of the crash risk associated with EDS and SA were tested for publication bias using the trim-and-fill technique because there were enough available data for these two factors. Evidence (Fig. 2) of publication bias was found in summary estimate of EDS problems and two new data points (represented by solid circles in Fig. 2) were added accordingly to make the funnel plot symmetric. After adjusting for publication bias, the summary odds ratio (its log form was represented by the solid diamond in Fig. 2) for having a crash in drivers with EDS reduced from 1.89 (95% CI: 1.49–1.92) to 1.72 (95% CI: 1.36–2.18). No publication bias in the estimate of crash risk associated with SA was indicated by the trim-and-fill analysis. In summary, for each sleep problem, information about the model used to generate effect size, the odds ratio and its 95% Table 4 Heterogeneity test results. Group

Q-value

df (Q)

p-value

I-squared (%)

EDS SA Acute sleepiness Insomnia

33.50 31.24 6.64 3.17

10 6 2 2

0.00 0.00 0.04 0.21

70.17 80.79 69.89 36.90

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Table 5 Meta-analysis results. Sleep Problem

Number of estimates Heterogeneity Model of analysis OR

EDS 11 Sleep apnea 7 Insomnia 3 Acute sleepiness 3

Yes Yes No No

RE RE FE FE

1.89 1.75 1.17 1.85

95% CI

Trim and fill analysis Data points added Adjusted for 95% CI publication bias

1.49–1.92 1.50–2.05 0.92–1.49 1.52–2.24

Performed Performed Not performed Not performed

2 0 Not applicable Not applicable

1.72 Not applicable Not applicable Not applicable

1.36–2.18 Not applicable Not applicable Not applicable

confidence interval, test results for trim and fill analysis and the adjusted odds ratio in case of publication bias, are listed in Table 5. In general, the increased accident risk for professional drivers associated with EDS, SA and AS was modest while impact of insomnia on crash risk was not statistically significant. However, since summary estimates of risk based on less than five studies must be regarded as highly uncertain (Elvik, 2013), more research on insomnia and acute sleepiness is required for a firmer conclusion. 3.3.2. Other sleepiness-related risk factors Other sleepiness-related risk factors were only explored by a limited number of studies and thus it was not appropriate to summarize their findings by meta-analysis. 3.3.2.1. Sleep debt. There is no unanimous definition of sleep debt. Carter et al. (2003) considered that drivers suffered from sleep debt when they could not get as many sleep hours as could be reasonably expected. Based on this definition, they found that professional drivers reported proportionally more sleep debt than the non-professional drivers. Perez-Chada et al. (2005) contended that drivers had insufficient sleep when they slept 2-h less on weekdays compared with the sleep hours on weekends and found 86.9% of professional drivers had such a problem. Road risk associated with sleep debt was studied by Howard et al. (2004). Using 7.1–8 sleep hours as the reference group, it was found that drivers who slept less than 5 h or slept 5.1–6 h were not at higher risk of being involved in accidents, however, they did have higher lever of excessive daytime sleepiness. 3.3.2.2. Snoring. Snoring is considered to be a risk factor for sleep apnea and is usually used as an accessory diagnostic marker. However, independent of the existence of sleep apnea, it can also induce sleepiness by itself (Gottlieb et al., 2000). The self-reported snoring frequency is usually used to determine the existence and severity of a snoring problem. In a survey organized by Carter et al. (2003), snoring frequency responses including ‘‘never; sometimes; sometimes per month’’ were classified as ‘‘no snoring’’ and responses including ‘‘sometimes per week; daily’’ were classified as ‘‘snoring’’. No significant increased crash risk for the snoring group was found. However, Perez-Chada et al. (2005) defined frequent snorers as those who snored more than 3 nights per week and stated drivers who were frequent snorers were significantly more likely to be involved in accidents or near accidents (OR = 1.73; 95% CI: 1.23–2.44).

Fig. 2. Funnel plot of risk associated with EDS adjusted for publication bias. Note: Open circles are the original data; solid circles are the imputed filled values. The observed point estimate in log units is shown as an open diamond at 0.64 (95% CI: 0.40–0.65), corresponding to an odds ratio of 1.89 (95% CI: 1.49–1.92); the imputed point estimate in log units is shown as a filled diamond at 0.54 (95% CI: 0.31–0.78), corresponding to an odd ratio of 1.72 (95% CI: 1.36–2.18).

although such association did not reach statistical significance (Souza et al., 2005). 3.3.3. Potential confounding factor impact Age and the obesity are two confounding factors whose influences on accidents are usually controlled when exploring association between sleepiness and on-road crashes. Previous studies on non-professional drivers found that older drivers were at higher crash risk than younger; however, evidence in recent years has found that when driving distance was controlled, age was no longer a significant risk factor (Hakamies-Blomqvist et al., 2002). For professional drivers, both Howard et al. (2004) and Souza et al. (2005) found a negative association for age and accident risk. Obesity has been reported to be associated with many sleeprelated problems and has been demonstrated to be a crash risk factor for non-professional drivers. Regarding the impact of obesity on professional drivers, Stoohs et al. (1994) found the above conclusions were also applicable. They found that non-obese drivers (BMI < 30 kg/m2) reported significantly fewer accidents and were less sleepy than obese truck drivers (p < 0.03). 4. Discussion

3.3.2.3. Sleep Quality. Using the Pittsburgh Sleep Quality Index as a measure of sleep quality, Sabbagh-Ehrlich et al. (2005) found that truck drivers with moderate to severe sleep quality problem were 2.9 times more likely to be involved in severe crashes, this conclusion was arrived at after adjusting for confounding effect of body mass index (BMI), driving experience and high blood pressure factors (p < 0.05). In another similar study, the results also suggested that poor sleep quality led to higher crash risk

4.1. Sleep apnea (SA) Two reviews have tried to explore the influence of sleep apnea (SA) on professional driver crash risks (Ellen et al., 2006; Tregear et al., 2009). At the time these two reviews were written, only two articles (Howard et al., 2004; Stoohs et al., 1994) were available and both of them found no association between SA and crash

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risk for professional drivers. Five studies published after 2009 were added to form the database in this review to analyze the impact of SA of on crash risk. Based on the random effects model, the impact found here showed a significant average OR of 1.68, which is a level that indicates a positive association between SA and the crash risk for professional drivers. A trial was then made to compare this association with results found for non-professional drivers. For non-professional drivers, the review work done by Ellen et al. (2006) identified that the majority of the studies (23 out of 27) found a statistically significant (2–3 times) increase in crash risk associated with sleep apnea. In another systematic review (Tregear et al., 2009), the metaanalysis based on 10 studies revealed that general drivers with SA were 2.43 (95% CI: 1.21–4.89) times more likely to be involved in on-road accidents than those without SA. One study that was not included in the above reviews found an adjusted OR of 6.0 (95% CI: 1.1–32) for sleep apnea based on interviews with a large group (n = 4002) of randomly selected drivers (Masa et al., 2000). Another recent study (Komada et al., 2009) also found an elevated risk of road traffic accidents (OR: 2.17; 95% CI: 1.30–3.65) for male drivers with SA in the Tokyo Metropolitan Area. In summary, contrary to expectation, it seems that the crash risk for professional drivers with SA (OR: 1.75; 95% CI: 1.50–2.05) is lower than for non-professional drivers with SA. However, 3 out 7 studies in this present review actually indicated a more than 2 times crash risk; but due to the large variance, they were assigned with less weight in meta-analysis, leading to a moderate final effect size. To generate a more accurate estimate, it is suggested more research focusing exclusively on professional drivers should be performed in the future. 4.2. Excessive daytime sleepiness (EDS) The estimates of crash risk for general drivers with EDS exhibit a large range. Chen and Wu (2010) reported a moderate effect size of OR = 1.62 (95% CI: 1.29–2.02) while Lucidi et al. (2013) found that drivers with EDS were 12 times more likely to be involved in single-vehicle accidents (OR: 12.5; 95% CI: 1.98–78.6). Unlike the large variation in estimates of EDS influence on nonprofessional drivers, effect size of EDS on professional driver crash risk has been estimated with high consistency across different studies. The majority (eight out of eleven studies) of the estimated odds ratios fell between 1 and 3. The only extreme estimate (OR = 5.78; 95% CI: 1.37–24.34) came from Karimi et al. (2013), probably due to the limited sample sizes as well as for having no control for possible confounding factors. 4.3. Acute sleepiness Acute sleepiness at the wheel has not been studied as much as other sleep problems, partially because it is impossible to test the sleepiness level at the time a collision happens. This factor can only be assessed after accidents based on driver memory and self-estimation, which makes it difficult to obtain a reliable measure due to the recall bias (Williamson et al., 2011). Karolinska Sleepiness Scale (Åkerstedt and Gillberg, 1990) and Stanford Sleepiness Scale (Liu et al., 2003) are two of the most widely used instruments, with relatively high validity, to test sleepiness level based on self-reported data. Acute sleepiness has been identified as a strong predictor of driving accidents among non-professional drivers from New Zealand, US and France (Connor et al., 2002; Cummings et al., 2001; Sagaspe et al., 2010). Multivariate logistic regressions were applied in all three studies and the estimated crash risks ranged from 2 to 14 times higher for drivers suffering from severe sleepiness at wheel.

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For professional drivers, meta-analysis results based on three available studies (Perez-Chada et al., 2005; Sabbagh-Ehrlich et al., 2005; Stuckey et al., 2010) indicated a moderately increased crash risk for drivers with acute sleepiness problem. However, this association was not strong due to the limited number of studies and further work is needed to improve understanding of the impact of sleepiness on crash risk for professional drivers. One study (Bunn et al., 2005) concerning acute sleepiness was not included in the meta-analysis since the case and control group were fatal accident and non-fatal accident respectively. In this study the adjusted OR was 21 with a wide confidence interval (95% CI: 4.17–106.07), indicating that professional drivers suffering from acute sleepiness were more likely to be involved in fatal accidents than in non-fatal accidents. This conclusion supports the findings referred to at the beginning of the introduction that sleepiness accounts for greater proportion of severe accidents than less serious crashes. 4.4. Limitations and future research direction One limitation of this study is that the number of reviewed studies, especially for insomnia and acute sleepiness, is at the low side. One reason is that few studies treat professional drivers as an independent research group, even though this group of drivers is prone to higher risk of suffering from sleepiness problems than non-professional drivers. Consequently, there is a need for high-quality studies focusing exclusively on professional drivers to generate a more comprehensive and accurate understanding of impact of sleepiness on traffic accidents. Another reason is that the incomplete information reporting makes it impossible to extract the effect size from some otherwise relevant studies. There are no general criteria about the kinds of information that should be reported in papers concerned with driver sleepiness and accidents. Based on the experience gained in performing this review work and on suggestions made by Fritz et al. (2012), it is proposed here that the following basic data and statistics should be included in papers or reports to provide sufficient information for possible subsequent meta-analysis work. First, the researcher should always report descriptive statistics such as sample sizes, means, and standard deviations and when there are different groups, the above data should be reported for each group. Second, when comparing two sets of data, Cohen’s d is recommended as the default effect size estimate. For those authors who use t-tests, ANOVA, or correlation, both the values of the test statistic (e.g. t-value; F-ratio; or r-value) and the significance indicator (p-value) should be reported so that readers can calculate Cohen’s d or other types of effect sizes for themselves. For studies applying regression, it is recommended that the regression coefficient (e.g. odds ratio for logistic regression) and its confidence interval should be reported. The above statistics are the minimum information to allow transformations between the different types of effect sizes. Third, It is recommended here that all results should be reported thoroughly, whether they are significant or not. The second limitation is that the severity of sleep problems was not considered when generating the summary effect sizes because most studies did not report on the basis of such a classification. Since there is evidence that severity of sleepiness was associated with an increased on-road accident risk for general drivers (Tregear et al., 2009), it is suggested that future research for professional drivers should take sleep problem severity into consideration. 5. Conclusions This paper investigates the risk of road crashes associated with sleepiness for professional drivers by reviewing 15 related studies

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in literature. The meta-analysis results showed that the increase in the risk of crash involvement was modest for excessive daytime sleepiness (EDS), sleep apnea and acute sleepiness, but not significant for insomnia. Contrary to expectation, professional drivers were not at higher increased risk than general drivers when they suffered from sleep apnea and excessive daytime sleepiness. Acknowledgements This research was supported by the Hong Kong PhD Fellowship Scheme (HKPFS) from the Research Grants Council (RGC) of Hong Kong. The authors would like to acknowledge the contributions of Yutao Ba and Lin Lv for helping in data extraction. References Åkerstedt, T., Gillberg, M., 1990. Subjective and objective sleepiness in the active individual. Int. J. Neurosci. 52, 29–37. Akkoyunlu, M.E., Alt, N., Kart, L., Atalay, F., Ornek, T., Bayram, M., Tor, M., 2013. Investigation of obstructive sleep apnoea syndrome prevalence among longdistance drivers from Zonguldak, Turkey. Multidiscip. Respir. Med. 8, 10–10. Amra, B., Dorali, R., Mortazavi, S., Golshan, M., Farajzadegan, Z., Fietze, I., Penzel, T., 2012. Sleep apnea symptoms and accident risk factors in Persian commercial vehicle drivers. Sleep Breath., 1–5. Bland, J.M., Altman, D.G., 2000. Statistics notes: the odds ratio. BMJ. Br. Med. J. 320, 1468. Borenstein, M., Hedges, L., Higgins, J., Rothstein, H., 2005. Comprehensive MetaAnalysis Version 2. Biostat, Englewood, NJ. Borenstein, M., Hedges, L.V., Higgins, J.P., Rothstein, H.R., 2009. Computing Effect Sizes for Meta-analysis. John Wiley & Sons, Ltd, Chichester. Borenstein, M., Hedges, L.V., Higgins, J.P., Rothstein, H.R., 2011. Introduction to Meta-Analysis. Wiley. Bunn, T.L., Slavova, S., Struttmann, T.W., Browning, S.R., 2005. Sleepiness/fatigue and distraction/inattention as factors for fatal versus nonfatal commercial motor vehicle driver injuries. Accid. Anal. Prevent. 37, 862–869. Caird, J.K., Willness, C.R., Steel, P., Scialfa, C., 2008. A meta-analysis of the effects of cell phones on driver performance. Accid. Anal. Prevent. 40, 1282–1293. Carter, N., Ulfberg, J., Nyström, B., Edling, C., 2003. Sleep debt, sleepiness and accidents among males in the general population and male professional drivers. Accid. Anal. Prevent. 35, 613–617. Chen, Y.-Y., Wu, K.C.-C., 2010. Sleep habits and excessive daytime sleepiness correlate with injury risks in the general population in Taiwan. Injury Prevent. 16, 172–177. Chipman, M., Jin, Y.L., 2009. Drowsy drivers: the effect of light and circadian rhythm on crash occurrence. Saf. Sci. 47, 1364–1370. Connor, J., Norton, R., Ameratunga, S., Robinson, E., Civil, I., Dunn, R., Bailey, J., Jackson, R., 2002. Driver sleepiness and risk of serious injury to car occupants: population based case control study. BMJ 324, 1125. Connor, J., Whitlock, G., Norton, R., Jackson, R., 2001. The role of driver sleepiness in car crashes: a systematic review of epidemiological studies. Accid. Anal. Prevent. 33, 31–41. Cummings, P., Koepsell, T.D., Moffat, J.M., Rivara, F.P., 2001. Drowsiness, countermeasures to drowsiness, and the risk of a motor vehicle crash. Injury Prevent. 7, 194–199. Dawson, D., Reid, K., 1997. Fatigue, alcohol and performance impairment. Nature 388, 235. de Pinho, R.S., da Silva-Junior, F.P., Bastos, J.P.C., Maia, W.S., de Mello, M.T., de Bruin, V.M., de Bruin, P.F.C., 2006. Hypersomnolence and accidents in truck drivers: A cross-sectional study. Chronobiol. Int. 23, 963–971. Dinges, D.F., 1995. An overview of sleepiness and accidents. J. Sleep Res. 4, 4–14. Duval, S., Tweedie, R., 2000. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455– 463. Ellen, R., Marshall, S.C., Palayew, M., Molnar, F.J., Wilson, K.G., Man-Son-Hing, M., 2006. Systematic review of motor vehicle crash risk in persons with sleep apnea. J. Clin. Sleep Med. 2, 193–200. Elvik, R., 2011. A framework for a critical assessment of the quality of epidemiological studies of driver health and accident risk. Accid. Anal. Prevent. 43, 2047–2052. Elvik, R., 2013. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid. Anal. Prevent. 60, 254–267. Fritz, C.O., Morris, P.E., Richler, J.J., 2012. Effect size estimates: current use, calculations, and interpretation. J. Exp. Psychol.: Gen. 141, 2–18. Gottlieb, D.J., Yao, Q., Redline, S., Ali, T., Mahowald, M.W., 2000. Does snoring predict sleepiness independently of apnea and hypopnea frequency? Am. J. Respir. Crit. Care Med. 162, 1512–1517.

Hakamies-Blomqvist, L., Raitanen, T., O’Neill, D., 2002. Driver ageing does not cause higher accident rates per km. Transport. Res. Part F: Traffic Psychol. Behav. 5, 271–274. Higgins, J., Thompson, S.G., 2002. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558. Howard, M.E., Desai, A.V., Grunstein, R.R., Hukins, C., Armstrong, J.G., Joffe, D., Swann, P., Campbell, D.A., Pierce, R.J., 2004. Sleepiness, sleep-disordered breathing, and accident risk factors in commercial vehicle drivers. Am. J. Respir. Crit. Care Med. 170, 1014–1021. Høye, A., Elvik, R., 2010. Publication bias in road safety evaluation. Transport. Res. Rec.: J. Transport. Res. Board 2147, 1–8. Hui, D.S., Ko, F.W., Chan, J.K., To, K.W., Fok, J.P., Ngai, J.C., Chan, M.C., Tung, A., Chan, D.P., Ho, C.W., 2006. Sleep-disordered breathing and continuous positive airway pressure compliance in a group of commercial bus drivers in Hong Kong. Respirology 11, 723–730. Hunter, J.E., Schmidt, F.L., 2004. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings. SAGE Publications Incorporated. Karimi, M., Eder, D.N., Eskandari, D., Zou, D., Hedner, J.A., Grote, L., 2013. Impaired vigilance and increased accident rate in public transport operators is associated with sleep disorders. Accid. Anal. Prevent. 51, 208–214. Komada, Y., Nishida, Y., Namba, K., Abe, T., Tsuiki, S., Inoue, Y., 2009. Elevated risk of motor vehicle accident for male drivers with obstructive sleep apnea syndrome in the Tokyo metropolitan area. Tohoku J. Exp. Med. 219, 11–16. Leechawengwongs, M., Leechawengwongs, E., Sukying, C., Udomsubpayakul, U., 2006. Role of drowsy driving in traffic accidents: a questionnaire survey of Thai commercial bus/truck drivers. J. Med. Assoc. Thai. 89, 1845–1849. Liu, G.-F., Han, S., Liang, D.-H., Wang, F.Z., Shi, X.-Z., Yu, J., Wu, Z.-L., 2003. Driver sleepiness and risk of car crashes in Shenyang, a Chinese northeastern city: population-based case-control study. Biomed. Environ. Sci.: BES 16, 219–226. Lucidi, F., Mallia, L., Violani, C., Giustiniani, G., Persia, L., 2013. The contributions of sleep-related risk factors to diurnal car accidents. Accid. Anal. Prevent. 51, 135– 140. Masa, J.F., Rubio, M., Findley, L.J., 2000. Habitually sleepy drivers have a high frequency of automobile crashes associated with respiratory disorders during sleep. Am. J. Respir. Crit. Care Med. 162, 1407–1412. Naughton, M., Pierce, R., 1991. Sleep apnoea’s contribution to the road toll. Aust. N. J. Med. 21, 833–834. Öz, B., Özkan, T., Lajunen, T., 2010. An investigation of the relationship between organizational climate and professional drivers’ driver behaviours. Saf. Sci. 48, 1484–1489. Pack, A.I., Maislin, G., Staley, B., Pack, F.M., Rogers, W.C., George, C.F., Dinges, D.F., 2006. Impaired performance in commercial drivers role of sleep apnea and short sleep duration. Am. J. Respir. Crit. Care Med. 174, 446–454. Perez-Chada, D., Videla, A.J., Flaherty, O., Palermo, M.E., Meoni, P., Sarchi, J., Khoury, M.I., Duran-Cantolla, M., 2005. Sleep habits and accident risk among truck drivers: a cross-sectional study in Argentina. Sleep – New York Westchester 28, 1103. Philip, P., Sagaspe, P., Lagarde, E., Leger, D., Ohayon, M.M., Bioulac, B., Boussuge, J., Taillard, J., 2010. Sleep disorders and accidental risk in a large group of regular registered highway drivers. Sleep Med. 11, 973–979. Phillips, R.O., Sagberg, F., 2013. Road accidents caused by sleepy drivers: update of a Norwegian survey. Accid. Anal. Prevent. 50, 138–146. Punjabi, N.M., 2008. The epidemiology of adult obstructive sleep apnea. Proc. Am. Thoracic Soc. 5, 136–143. Razmpa, E., Sadegh Niat, K., Saedi, B., 2011. Urban bus drivers’ sleep problems and crash accidents. Indian J. Otolaryngol. Head Neck Surg. 63, 269–273. Road and Traffic Authority, N., 2001. Road Traffic Accidents in New South Wales 2000. Sabbagh-Ehrlich, S., Friedman, L., Richter, E., 2005. Working conditions and fatigue in professional truck drivers at Israeli ports. Injury Prevent. 11, 110–114. Sagaspe, P., Taillard, J., Bayon, V., Lagarde, E., Moore, N., Boussuge, J., Chaumet, G., Bioulac, B., Philip, P., 2010. Sleepiness, near-misses and driving accidents among a representative population of French drivers. J. Sleep Res. 19, 578–584. Souza, J.C., Paiva, T., Reimão, R., 2005. Sleep habits, sleepiness and accidents among truck drivers. Arquivos de Neuro-Psiquiatria 63, 925–930. Stoohs, R.A., Guilleminault, C., Itoi, A., Dement, W.C., 1994. Traffic accidents in commercial long-haul truck drivers: the influence of sleep-disordered breathing and obesity. Sleep – New York 17, 619–619. Stuckey, R., Glass, D.C., LaMontagne, A.D., Wolfe, R., Sim, M.R., 2010. Risk factors for worker injury and death from occupational light vehicles crashes in New South Wales (Australia). Am. J. Ind. Med. 53, 931–939. Tregear, S., Reston, J., Schoelles, K., Phillips, B., 2009. Obstructive sleep apnea and risk of motor vehicle crash: systematic review and meta-analysis. J. Clin. Sleep Med.: JCSM: Official Publ. Am. Acad. Sleep Med. 5, 573. Vaa, T., 2003. Impairments, diseases, age and their relative risks of accident involvement: results from meta-analysis. Vennelle, M., Engleman, H.M., Douglas, N.J., 2010. Sleepiness and sleep-related accidents in commercial bus drivers. Sleep Breath. 14, 39–42. Wang, J.-S., Knipling, R.R., Blincoe, L.J., 1996. Motor vehicle crash involvements: a multi-dimensional problem size assessment. Information Management Consultants. Williamson, A., Lombardi, D.A., Folkard, S., Stutts, J., Courtney, T.K., Connor, J.L., 2011. The link between fatigue and safety. Accid. Anal. Prevent. 43, 498–515.